Method and apparatus for pattern analysis

ABSTRACT

A technique and apparatus for two dimensional pattern analysis utilizing a transform of the pattern enables the extraction of desired pattern information by means of spatial filtering in accordance with known human visual system processing. Two dimensional spatial frequencies resulting from the transform are acted on by either anisotropic or uniquely used conventional filters to extract one, two and three dimensional pattern information from spatial frequency subsets to determine general form, edge, texture and depth information for detection, identification and classification of objects in simple or complex scenes.

BACKGROUND OF THE INVENTION

The invention relates generally to pattern analysis and moreparticularly to two dimensional pattern analysis performed byattenuating and isolating spatial frequency subsets of a transform ofthe pattern. Transform attenuation corresponding to the variations ofhuman contrast sensitivity over 360° orientation may be used to bias twodimensional transform data such that one, two and three dimensionalpattern information in spatial frequency subsets may be extracted byusing conventional bandpass spatial filters. In addition, theconventional bandpass spatial filter may provide depth information byuniquely using it in combination with the transform with or without thetransform attenuation corresponding to variations of human contrastsensitivity.

The prior art has attempted similar two dimensional pattern analysisusing contrast sensitivity data; however, any contrast sensitivityattenuation attempted was performed isotropically over 360°. Also,special binary valued, wedge-type filters were used to obtain spatialfrequency information at various orientations. However, these devicesrequired complex means for extracting the pattern elements to createspecial frequency signature rather than enabling the ulitization ofsimple thresholding to obtain the same result for many tasks. Basicpattern information previously required heuristic low pass bandpassspatial filters. None of the prior art devices were able to obtain largeamounts of relevant pattern information from two dimensional spatialfrequency information because the attenuation characteristics fororientation and spatial frequency and relevant spatial frequency bandswere not recognized or determined. Furthermore, third dimensional depthinformation in terms of spatial frequency subsets and reconstructedintensity gradients has been beyond the realm of the two dimensionalpattern analysis systems heretofore contemplated. Finally, twodimensional pattern analysis previously developed does not have theunification or parsimony of the methods outlined herein.

SUMMARY OF THE INVENTION

A method and apparatus for two dimensional pattern analysis utilizingtwo spatial filter types is presented. The first spatial filter type isunique and attenuates the two dimensional pattern transform datacorresponding to human contrast sensitivity values. This represents thehuman physiological visual system asymmetric resolution of patterninformation over 360° of orientation. The asymmetric spatial filterprovides a great amount of pattern information since it is orientationsensitive, especially in automatically separating similar textureelements and forms from backgrounds that differ only in orientation. Theattenuation characteristics of the asymmetric spatial filterautomatically provides, upon reconstruction of the attenuated transform,intensity values that allow the use of simple thresholding to isolatesimilar pattern elements differing in orientation over a continuum ofintensity values. The second spatial filter type is a conventionalbandpass filter. The attenuation characteristics of the anisotropicspatial filter allows basic pattern form information to be extracted bya conventional bandpass spatial filters based upon energy of thetransform components resulting in less transform information to bestored in a memory and processed for the classification scheme.Translating conventional spatial filters over the two dimensionaltransform to isolate spatial frequency subsets, according to energycontained therein, renders possible the extraction of three dimensionalinformation by correlating that information with similar informationstored in memory or by retransforming just the isolated spatialfrequency subset. Third dimension depth information is in terms ofpattern intensity with concomitant shape changes can be seen explicitlyin a retransformed pattern. This technique of translated spatial filtersalso allows the extraction and biasing of selected original pattern edgefeatures in the reconstructed pattern. In addition, the very low spatialfrequency information, e.g., the fundamental spatial frequency also isused to extract third dimensional depth information over large patternareas, e.g., depth information from different texture gradients.

Thus, it is the primary object of this invention to develop a method andapparatus that enables the isolation and extraction of edge, form andtexture, and three dimensional (depth) information from two dimensionalpatterns within the context of human visual information processing.

It is an object of this invention to obtain edge information from a twodimensional display by the translation of a conventional bandpass filterover two dimensional spatial frequency subsets.

It is another of this invention to obtain shape changes and intensitygradients from a two dimensional display which are correlated topatterns in depth.

It is still another object of this invention to provide a systemutilizing a two dimensional Fourier or other transform which is coupledto a spatial filter having a modulation transfer function correspondingto human contrast sensitivity values and a conventional spatial filterto get improved correlation with the human visual system processing formore accurate selection of important spatial frequencies to be extractedby the conventional bandpass spatial filters.

It is a further object of this invention to obtain edge, form, textureand depth information from a two dimensional transform which isfiltered, wherein the transform and filter may be either digitally oroptically generated.

It is a still further object of this invention to provide a method andapparatus for two dimensional pattern analysis which corresponds to thatperformed by the human visual system in that spatial filtering is madeto correspond to the human physiological visual system contrastsensitivity.

Another object of this invention is to obtain a two dimensional patternanalysis which utilizes a transform of the pattern into bands from whichsubsets may be extracted.

Still another object of this invention involves the obtaining of forminformation from a two dimensional pattern with a minimal amount ofspatial frequencies.

A further object of this invention involves two dimensional patternanalysis wherein a transform is obtained having the fundamentalfrequency utilized for intensity information relating to depth; themid-low frequencies providing form and edge information; while the highfrequencies of the transform provides fine details.

A still further object of this invention involves the provision of amodulation transfer function (MTF) spatial filter for use with atransform of a two dimensional pattern wherein the MTF filter biases thefrequencies of the transform in accordance with human contrastsensitivity with anisotropic characteristics.

It is another object of this invention to provide a two dimensionalpattern analysis system whereby orientation sensitivity of a spatialfilter provides edge information and specific form extraction.

It is still another object of this invention to provide two dimensionalpattern transform analysis techniques. Although some similar analysiscan be performed mathematically in the space domain, space domaintechniques such as convolution lose space invariance (translationalinvariance) and analytical power for separating information.

It is a further object of this invention to provide a technique andapparatus capable of automatically detecting classifying and identifyingtargets contained in a two dimensional data processors such as isprovided by high resolution radar imagery.

It is a still further object of this invention to provide an apparatusfor pattern analysis which is easy and economical to produce comprisedof conventional currently available materials that lend themselves tostandard mass production manufacturing techniques.

These and other advantages, features and objects of the invention willbecome more apparent from the following description taken in connectionwith the illustrative embodiment in the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating the various steps and apparatusrequired for performance of two dimensional pattern analysis in order toobtain edge, texture, form and depth;

FIG. 2 is a two dimensional intensity pattern of a letter G formed bydots;

FIG. 3 is the upper half of a Fourier transform matrix representing theamplitude of the spatial frequency components in the two dimensionaltransform plane of the dotted letter G of FIG. 2;

FIG. 4 is the normalized Fourier transform of FIG. 3;

FIG. 5 is a typical modulation transform function set of values for a 64by 64 array;

FIG. 6 is a digital representation of the normalized amplitude spectrumof the dotted G represented in the Fourier transform of FIG. 4 after ithas been acted on by the MTF filter of FIG. 5;

FIG. 7 is the inverse transform of FIG. 6 (with the bottom halfpresented) in order to illustrate the reconstructed dotted G and theeffects of the MTF filter on the original dotted G;

FIG. 8 is a digital representation of the dotted G representation ofFIG. 7 after further spatial filtering by conventional low pass spatialfilter; and

FIG. 9 is a block diagram illustrating target detection andidentification techniques.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

The technique and apparatus for two dimensional pattern analysis isillustrated below in conjunction with specific Figures to whichreference will be made. It is contemplated that both feature extraction,detection and classification will be encompassed within the patternrecognition aspects of the invention. Since feature extractiontechniques of the human visual system have been investigated, thetechnique and apparatus of this invention is made to correspond to thehuman visual system. The correspondence of the apparatus of thisinvention to the human visual system has been provided by allocating theprocessing devices of the human to specific apparatus. For example, thevisual system is divided into three basic units, i.e., preprocessing,transforming and classification. The preprocessing concerns theprocessing of the pattern, p(x,y), from the lens and retina through theprimary visual cortex, which would be represented in the apparatus ofthis system by a two dimensional, anisotropic, spatial filter (MTF) -H_(MTF) (u,v) -. The transforming step would be performed by a twodimensional Fourier transform P(u,v) which operates on the preprocesseddata. Classification is obtained by the selection of subsets of theFourier transform by means of conventional bandpass spatial filterH_(BP) (u,v) techniques that are correlated to stored spatial frequencysubsets of patterns for detection classification or identification. Thereconstructed pattern, P_(r) (x,y), is used for further analysis andthresholding techniques.

In the foregoing paragraph p(x,y) is the two dimensional intensitydistribution of the pattern where x,y are the space coordinates in thetwo dimensional pattern plane. H_(MTF) (u,v) is the percent contrastsensitivity values over the two dimensional transform plane (u,v).##EQU1## H_(BP) (u,v) is a representation of the bandpass filter whereselected values of u,v would be equal to one for passed or correlatedspatial frequencies and all others would be equal to zero. The pattern##EQU2## where X = 2u + 1; Y = 2v + 1 and ##EQU3## are the spatialfrequencies in cycles per unit length, comprises the reconstructed twodimensional pattern after the Fourier transform and both MTF andbandpass spatial filtering.

Utilization of the principles of this invention can be achieved withrespect to an understnading of the block diagram of FIG. 1. In thisFigure there is shown a two dimensional pattern at 10 which may beeither digitally or optically presented, for example, as two dimensionalradar data or a photograph. The transform at 12 results in separatefrequency bands collected into frequency subsets, each of which containsspecific pattern form or varieties of pattern form information. Thetransform may be performed by conventional optical means or by means ofelectronic circuitry such as a digital computer. The discussion of thisinvention will be related to a centro-symmetric Fourier transform sincethis is the easiest type to visualize and is found in the transformplane of an optical system. Other types of transform may be used and,like the two dimensional pattern, may be either optically orelectronically, i.e., digitally represented. The transform 12 takes thetwo dimensional intensity information from the pattern at 10 andanalyzes it to a multiplicity of frequency bands. The high frequencybands contains the fine detail while the low frequency bands containgross form. When digital Fourier transforms are used, a numericalprintout describes discrete frequencies whereas an optical Fouriertransform provides infinite spatial frequencies.

Spatial frequency is a single value comprised of a real and an imaginarypart whose magnitude ##EQU4## is represented as a function of positionis the u,v transform plane. The central spatial frequency D.C.represents the average energy across the pattern.

Each band increases in spatial frequency as one goes from the center ofthe transform (D.C.) and the same spatial frequency value is in aradially concentric pattern in an optical system or in a square shapepattern in a digital rectangular coordinate system. Collections ofeither one or more of the spatial frequencies in the frequency bandsequally or unequally over the two dimensional transform plane are calledsubsets.

The MTF (Modulation Transfer Function) spatial filter is shown at 14 onFIG. 1 and is constructed so as to bias or attenuate particularfrequencies at particular orientations. The attenuation is asymmetricand corresponds to the average value of (percent) contrast sensitivityfor various angles in accordance with the human physiological visualsystem resolution. The MTF filter data ulitized in the pattern analysisof this invention relates to 0°, 45° and 90° angles with interpolationproviding for values for orientations other than those obtained bymeasurement.

The spatial filter at 16 would be a conventional bandpass filter eitherisotropic or anisotropic depending upon its use as will be explainedinfra.

Examples of the items described relative to the diagram of FIG. 1 willbe illustrated with respect to FIGS. 2-8. The two dimensional intensitypattern of the letter G formed by dots is digitally represented in FIG.2. The blanks represent zero intensity, and digits from 1 through 9 aregiven their intensity value. In this and other digital representations,the value of 10 would be printed out as zero and values 0.1 to 1 wouldbe represented as a dot.

The transform 12, the lower half of which is omitted to avoidduplication since the transform is symmetric, is illustrated in FIG. 3and provides a digital representation of discrete Fourier amplitudetransform values of the pattern of FIG. 2. FIG. 4 is a digitalrepresentation of the normalized transform of FIG. 3 also with the lowerhalf removed for simplicity. The MTF spatial filter 14 of FIG. 1, whichis always anisotropic and is represented digitally in FIG. 5 for onlyone quadrant of a 64 by 64 array, since the remaining quadrants can beachieved by appropriate replication in the other three quadrants. Itshould be noted at this point that the MTF filters can have differentvalues depending upon the data obtained from the human visual system.That which is shown is for high sensitivity at low spatial frequencies.When the two dimensional pattern of FIG. 2 has been transformed andnormalized as illustrated in FIGS. 3 and 4 and had the MTF filter ofFIG. 5 applied thereto, the resultant of FIG. 6 provides arepresentation of the attenuated, normalized amplitude spectrum of thedotted G. The reconstructed G pattern by the inverse Fourier transformis illustrated in FIG. 7 which demonstrates the application of the MTFfilter on the original G pattern. Note that the dots are still resolvedand that the blurring corresponds to what an observer reports whenviewing a photograph constructed from that data. Further conventionalbandpass spatial filtering which allows only the first four spatialfrequencies to be used in reconstruction of the G pattern by a Fouriertransform results in FIG. 8 where a general G form inherent in thedotted G form has been extracted.

The dot letter G for which examples were given in FIGS. 2 through 8 wasrelated to the obtaining of general G form information from the twodimensional transform. Edge, texture and depth information may also beobtained from transform data. In order to obtain edge information wewould use the pattern 10 of FIG. 1 to which the transform 12 has beenobtained, and energy normalized (divide each spatial frequency by theD.C. term). At this point the MTF spatial filter 14 could be utilized tobias the edges according to orientation; however, the output from thetransform could be sent directly to a conventional mid to high frequencybandpass spatial filter 16 for unbiased edges according to orientation.This would result in a pattern outline upon reconstruction. When the MTFfilter is utilized, its output could go to the conventional spatialfilter 16, which is a mid to high frequency bandpass filter, to extractspatial frequencies for correlation with stored spatial frequencies in amemory or classification device 18. This would allow detection andidentification based upon edge pattern features. If the mid to highfrequency bandpass filter is isotropic it would best provide form edgeoutline, whereas anisotropic filters are used for edges at desiredorientations. Translation of the spatial filters over the transformplane according to energy in frequency orientation subsets may be usedin order to obtain edges at one, two or more orientations. When the midto high frequency bandpass filter is used for reconstruction, theinverse transform is used instead of the correlation as previouslydefined. The transform, MTF and bandpass filter steps would be the sameas previously described, however, the inverse transform as illustratedat 20 of FIG. 1 would provide highlighted edges and would allow the useof a threshold device 22 which would select intensity ranges by valuesto separate highlighted edge information from other pattern information.The threshold edges may be used for inputs for form or textureprocessing. If the transform MTF filter is reconstructed by an inversetransform determination and effects and pattern resolution of the humanvisual system physiological filtering are obtained.

To obtain form we would, as described by the edge informationprocessing, transform the pattern of 10 at 12 and energy normalize itand also use an MTF filter to attenuate the background from the form orskip the MTF step for unattenuated background. Here we would use a lowfrequency bandpass filter 16 without the fundamental or a few of thelowest frequencies to obtain the basic form information for correlationat 18 with similar stored spatial frequencies in the memory to detectand identify the form. If the filter 16 is isotropic and translatedaccording to the energy in the frequency orientation subsets, threedimensional forms in memory may be correlated to forms in depth.Reconstruction would utilize the low frequency bandpass filter where theimage is transformed, spatially filtered by the MTF filter and appliedto an inverse transform 20 to reconstruct patterns where the basis forcorrelation here would be observed from human analysis or thresholdtechniques that provide a separation of form information from otherpattern information to be provided at 22. To determine the effects andpattern resolution of a human visual system physiological filtering onewould transform the image after MTF spatial filtering and inversetransform.

Texture analysis is divided into two systems. Where the texture elementsdiffier in slope, intensity and in size one would apply a transform 12to the two dimensional pattern 10 and normalize it, apply the MTF filterto form texture clusters and elminate the MTF filter where there is notexture cluster formation desired. A mid high frequency bandpass filter16 is used to extract spatial frequencies for correlation with theclassification system 18. An isotropic filter would be used for textureband whereas an anisotropic spatial filter would be used for textureclusters differing in slope. Reconstruction by an inverse transform at20 could be achieved when a transform is applied to an image MTFfiltered. The inverse transform is used to reconstruct the filterpattern so that texture clusters are formed for human analysis orthreshold techniques could be used for separating texture clusters fromother pattern information. The cluster forms may be used as inputs forform processing.

When texture elements differ only in slope or shape from other formshere we would transform an applied MTF filter to attenuate texture andform elements that differ in slope and shape or eliminate this stepwhere no pattern segregation exists in terms of slope. The conventionalspatial filtering of the mid high frequency range here would extractspatial frequencies for correlation with stored spatial frequencies in amemory for detection and identification. An isotropic spatial filterwould be used after the MTF filter whereas an anisotropic spatial filterwould be used to extract texture element according to the energy atvarious orientations. The mid high frequency bandpass filter could haveits output applied to the inverse transform for reconstruction when theMTF has been used. The inverse transform allows for observation oftexture elements differing in intensity according to slope and shape.However, threshold techniques which isolate texture elements accordingto slope and shape by intensity variations could be used to segregatethese texture elements and provides an input for further processing.Determination of effects of pattern resolution of human visualphysiological filtering could be obtained by utilizing the transform 12spatial filter 14 and inverse transform 20.

Depth information in planes over large window areas uses the transform12 on the pattern 10 and may optionally use the spatial filter 14 wherea low frequency bandpass filter at 16 removes the fundamental frequencyfor correlation at 18 with stored spatial frequencies in a memory todetermine if depth planes are present. Anisotropic filters would be usedfor oriented depth planes whereas isotropic bandpass filters wouldprovide the usual depth information. If construction via an inversetransform 20 is desired the transform at 12 with or without step 14would be applied to the inverse transform. The phase terms of thefundamental spatial frequency or higher frequencies are used to phaselock high spatial frequency information over selected depth planes forform isolation. Here we would select high frequencies having the samephase terms as determined by the very low spatial frequencies. Thus, theinverse transform would reconstruct the filter pattern and the depthplane and forms could be observed for human analysis or by utilizing thethreshold techniques illustrated in the flow diagram in block 22.

Where the depth information is a function of pattern intensityvariations of smaller forms we would apply the transform 12 to thepattern 10 to obtain an energy normalized pattern with or without theMTF filter. A mid to high frequency bandpass filter would be used,isotropic or anisotropic, translated over high energy subsets accordingto energy contained in the transform domain. Either correlation of thespatial frequency with similar information stored in memory orreconstruction via an inverse transform may be accomplished fordetection, identification, or threshold techniques. The spatial filterbandwidths may be generally determined from the half-power of thepattern elements, to be detected, classified or identified, for example,a large form filling a 32 by 32 pattern element window may use abandpass filter of f=2, 4 cycles per picture width to capture its basicform.

The detection, classification and identification of objects is performedin the transform domain by comparing spatial frequency subsets from theinput pattern with similar information stored in memory. This comparisonmay be done by using a matched spatial filter or cross-correlating theinput spatial frequencies with stored spatial frequencies.

Matched spatial filtering, used primarily for detecting and locatingobjects in complex scenes, is usually used for optical processingwhereas cross-correlation is usually used in the digital processing.Those terms may be used for either optical or digital processing and areequivalent, except that a matched spatial filter, if followed by aninverse transform, results in an intensity value whose value andposition in the reconstructed plane represent the degree of comparisonand location of the detected object.

Maximizing cross-correlation is equivalent to minimizing Euclideandistance (d) between two spatial frequency subsets. ##EQU5## where M & Nare the number of spatial frequencies in the x and y directions,respectively, used for comparison.

Re_(p) = real part of transform of prototype in memory

Re_(i) = real part of transform of the input pattern

Im_(p) = imaginary part of the transform of the prototype in memory

Im_(i) = imaginary part of the input pattern

This Euclidean distance metric may be normalized and its value used torank order and thus provide a quantitative measure of how similar theinput pattern is to the pattern in memory.

These are linear classification schemes. Non-linear classificationschemes based on probability distribution or other decision criteria maybe used.

The phase lock technique is a unique method for segregating connectedobjects in simple or complex scenes using the fundamental or low spatialfrequencies to determine subcomponents of a whole pattern. For example,the patterns (assume a series of circles divided equally and unequallyby a straight line), each contains different fundamental spatialfrequency information directly and results in different intensitydistribution if inversely transformed from the fundamental spatialfrequency terms. (The fundamental spatial frequency term is defined asone whole cycle of the pattern width.) It is desirable to separate wholepatterns into subsections, for example, each section of the dividedcircle. This is accomplished by correlating the fundamental spatialfrequency information with the pattern subsections. If a subsectionexists from that information, then by selecting all the higher spatialfrequency terms containing similar phase as that of the subsectiondesired, one can obtain those isolated pattern subsections for furtheranalysis.

A basic flow diagram of the information processing techniques proposedto solve the complex scene analysis or target detection andidentification problem is presented in FIG. 9. The preprocessing,detection, and identification techniques are discussed in detail. Itshould be stressed that although the imagery will be initially processeddigitally, these techniques, especially the detection stage, may beoptimally performed using optical computers.

The preprocessing operations prepare the radar imagery for subsequenttarget detection and identification techniques. The radar imagery datamust be made compatible to the processing systems. Pre-digitized datastored on magnetic tape can be inputted directly. Film imagery will haveto be digitized by a scanniny system. Image gray scales will be eitherlinear or log normalized to maintain uniformity of multi-source imagery.The log transform will tend to enhance film contrast lost duringoriginal film processing. Further image processing may be desired suchas de-convolution of speculars. The preprocessing operations may beaccomplished on the entire image data before additional processing isundertaken or performed on each scan window before input to thedetection process. Some preprocessing steps could be possibly eliminatedif the detection and identification techniques are performed on theimage signal in either digital or film form.

The detection techniques are primarily designed to reduce identificationprocessing over image areas that are not of interest. The centralconcept of the detection technique is to use Priori inputs: desiredtargets to be detected; approximate target size from radar imagingparameters; and target false alarm levels to set up spatial matchedfilters for use in a cross-correlator. Thus the detector will be lookingonly for desired targets that exceed a predetermined target detectionerror.

A matched spatial filter (MSF) is known to be an optimal filter forseparating signals from noise in a linear system. In terms of radarimaging, the unwanted targets and background clutter, i.e., terrain, arethe noise to be separated from the desired targets, the signals. The MSFhas a transfer function with complex transmittance proportional to thecomplex conjugate of the Fourier transform of the signal. The MSFconverts the complex wavefront of the Fourier transform of the signalinto a plane wave which is focused into a bright point in the outputplane of the correlator whose position is directly related to the signalposition at the input plane. The spatial frequencies from the noise andundesired targets will be attenuated at low valued spatial frequencyregions of the MSF, thus reducing the energy of these noise sourcesdetected at the output plane. This concept had been demonstrated usuallyby detecting highly formatted patterns, e.g., a letter or words repeatedon a page of text. However, these demonstrations are really biased andMSF techniques perform poorly when one uses unformatted imagery. Thesimple reason for the failure is that there is too much noise versussignal energy in most real world scenes. There are two ways that areproposed to greatly reduce that problem. Firstly, the aperture of theinput plane of the correlator could be reduced (or the image enlarged)to make the size of the target large compared to the size of theaperture. In other words, scan smaller image areas rather than correlatethe entire image at one time. Secondly, previous research hasdemonstrated that basic shape information lies in a relatively smallband of spatial frequencies. Thus a bandpass MSF based upon gross targetshape will be generated and spatial frequencies generated by clutter,which are primarily high spatial frequency data, will not be allowed topass into the output plane and reduce the signal to noise ratio. Thesize of the scan window will be made as large as possible as to allowfor maximum detection areas with lowest tolerable signal to noiseratios. Target size differences will be compensated for by appropriatescaling of the bandpass MSF by extrapolation or interpolation from theradar imaging system scaling parameters. Target rotation in the imagespace produces a concomitant rotation in the Fourier transform domain.Previous research with rotated simple patterns using Fourier transformtechniques demonstrated successful pattern classification under ±30°rotation. Therefore, a solution to this problem is to rotate the MSF 360degrees in 12 increments of 30 degrees each. Another solution is tocreate one inclusive "OR" MSF from rotated MSFs to obviate any rotationrequirements during detection processing. The bandpass MSF will stilldetect the basic target shape even though some target degradation willoccur due to radar aspect angle changes unless the degradation is suchthat an observer could not detect the target. Most target degradationsdue to aspect angle involve relatively higher spatial frequencies. Thesetechniques enable quick, efficient target detection using MSF techniquesthat have previously failed.

The energy content of the correlation peaks provide a degree ofsimilarity between the detected target and the prototype (MSF) as wellas positional information that will be required for the targetidentification task. Correlation peak energy exceeding a preselectedfalse alarm level will indicate a detected target and will be reporatedas such. No targets detected in a scan window will cause the same windowto shift and detection processing to be initiated over the new scanwindow.

The correlation peaks in the output plane of the correlator provide thelocation of possible targets and lead the identification processing toonly those image areas of interest. Thus the more detailed and timeconsuming processing elements are concentrated only at selected targetareas, an important consideration for any possibility of real-timetarget identification in light of present digital transform processingspeeds. The central concept for target identification is the same asthat of the detection process except that more shape (feature)information is used in a more controlled decision process guided by thedetection results.

A tapered grid is centered over the detected target whose size,approximately 32 × 32 elements, will be a function of the target size.The fast Fourier transform computed over the grid is energy normalizedand spatial filtered. The target spectral components comprise the targetfeature vectors. Additional features that have been previouslydetermined to be required for correct target identification can beextracted from other target spectral components. Furthermore, targetcontext, e.g., background-texture, can be extracted from the higherspectral components. These additional features can be addedsyntactically (isolated and put into context) to the feature space whichwill be inputted to the classifier/identifier discriminant. The maindiscriminant used quite successfully for many diverse patternclassification tasks has been minimum Euclidean distance, equivalent tomaximizing correlation, of low spatial frequencies with storedprototypes. Other discriminants may be used. Previous patternrecognition tasks have required at most 7 × 7 or 49 low spatialfrequency values to be stored as prototypes, a quite small amount ofdata when compared to other feature extractor techniques such astemplate matching. The stored prototypes will be the result of averagedtraining set targets. It is important to realize that the radar imagingartifacts such as shadowing effects are included in the training setsand will not present any difficulty as long as the shadowing effects aresimilar for the same target. Since the images are a function of thetarget geometry, only variations due to radar look angle will presentdeviations in the image. Thus the technique presented here does notrequire optical fidelity of the image but shape (low spatial frequency)fidelity of the imaging system. It is emphasized that it is the lowspatial frequency target shape information that is used for thesedetection and identification techniques.

Each detected target will be processed by the preceding techniques andrank ordered against previously selected prototype targets. The closestprototype in a Euclidean distance sense to the target will be identifiedand reported as such with the normalized discriminant value providing ameasure of the degree of similarity between the prototype and thetarget. It may be desired to use decision trees to eliminate thedifferent classes of prototypes to be classified with any given target.For example, a swept wing aircraft can be identified as such by off axisFourier spatial frequencies which in turn could exclude non-swept wingaircraft from the classification process. Each detected target in eachscan window will be processed as just outlined until the complete imageis processed.

Although this invention has been described relative to particularembodiments, it will be understood that the invention is capable of avariety of alternative embodiments. Each of the steps or techniques maybe performed optically or electronically. For example, the translationcan be optically performed by physical movement or mathematicallyperformed by electronic or symbolic means. All of the elements are wellknown in the art and are standard with respect to methodology ortechnique except for the MTF filter, which makes information easier toobtain and use by providing improved sensitivity for thresholding, theutilization of translation for obtaining depth information, and phasecomparison with magnitude for complex scene or pattern analysis. Thenovel combination of method steps and means provides results notheretofore obtained. I intend to be limited only by the spirit and scopeof the appended claims.

I claim:
 1. Apparatus for analyzing a two dimensional patterncomprising,means for transforming the pattern into a spatial frequencydomain to relate harmonically the elements forming the patterninformation, means for filtering the transformed pattern substantiallyin accordance with the anisotropic attenuation characteristics of thehuman visual system, and means for bandpass filtering the filteredtransform to extract spatial frequency subsets from the said filteredtransform for feature analysis.
 2. An apparatus as defined in claim 1wherein said feature analysis is obtained by correlation of said spatialfilter subsets with stored information or for inverse transform andreconstructing band spatial frequency subsets for further analysis.
 3. Astatic, single stage, two dimensional, anisotropic, spatial frequencymagnitude filter having spatial frequency attenuation characteristicscorresponding substantially to the average contrast sensitivity of thehuman physiological visual system resolution at particular ambient lightconditions over 360° of viewing angle.